Watermarking
Watermarking is a technique used in stream processing and time-series systems to handle late-arriving data by defining the threshold between "on-time" and "late" events. It provides a way to balance processing completeness with result latency by establishing a point in time before which the system considers the input data complete enough to process.
How watermarking works
Watermarking establishes a moving threshold that tracks the progress of event time in a data stream. This threshold, called the watermark, lags behind the current processing time to accommodate data that arrives out of order or with delays.
The watermark helps the system decide when to:
- Trigger window computations
- Handle late-arriving data
- Close processing windows
- Release results
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Watermark types and strategies
Static watermarking
Fixed time delays that provide a simple but rigid approach:
- Set delay: "Wait 30 minutes after event time"
- Predictable behavior
- May not adapt well to varying data patterns
Dynamic watermarking
Adjusts the delay based on observed data patterns:
- Monitors actual data arrival patterns
- Adapts to changing conditions
- Better handles variable latency scenarios
Perfect watermarking
Theoretical ideal where:
- All data arrives in order
- No late events occur
- Rarely achievable in real systems
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Applications in time-series processing
Watermarking is essential for reliable event-time processing in:
Stream analytics
- Enables reliable windowed aggregations
- Supports accurate trend analysis
- Maintains processing order in distributed systems
Real-time monitoring
- Helps detect missing data
- Manages alert timing
- Balances completeness vs. latency
Example using time windows with watermarking:
Impact on system design
Performance considerations
- Affects processing latency
- Influences resource utilization
- Impacts system throughput
Architectural implications
- Requires buffer management
- Needs late data handling strategies
- Influences storage tiering decisions
Trade-offs
- Completeness vs. latency
- Resource usage vs. accuracy
- Complexity vs. flexibility
The effectiveness of watermarking depends heavily on understanding your data's characteristics and system requirements. Proper configuration requires balancing these factors against your specific use case needs.